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Analysis of Passive Memristive Devices Array: Data-Dependent Statistical Model and Self-Adaptable Sense Resistance for RRAMs

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3 Author(s)
Sangho Shin ; School of Engineering, University of California, Santa Cruz, CA, USA ; Kyungmin Kim ; Sung-Mo Kang

In this paper, a 2 × 2 equivalent statistical circuit model is presented to deal with sneak currents and random data distributions for design and analysis of n x m passive memory arrays of memristive devices. This data-dependent 2 × 2 model enables a broad range of analysis, such as the optimum detection voltage margin, with computational efficiency and no limit on the memory array size. We propose self-adaptable sense resistors that can find their statistical optimum values for reading stored data patterns by composing them with either a replica of a part of resistive random access memory (RRAM) array or a part of RRAM array itself. Self-adaptable resistors can increase the average voltage detection margin by 46%, and reduce the average current consumption by 14% for the case of a 128 × 128 passive array with OFF-to-ON resistance ratio of 103.

Published in:

Proceedings of the IEEE  (Volume:100 ,  Issue: 6 )